codeforces B. Tape

探讨了在限定条件下,如何使用最少长度的连续胶带片段覆盖所有断裂部分的算法问题。通过输入断裂位置和数量,算法计算并输出所需胶带的最小总长度。

B. Tapetime limit per test1 secondmemory limit per test256 megabytesinputstandard inputoutputstandard outputYou have a long stick, consisting of mm

segments enumerated from 11

to mm

. Each segment is 11

centimeter long. Sadly, some segments are broken and need to be repaired.You have an infinitely long repair tape. You want to cut some pieces from the tape and use them to cover all of the broken segments. To be precise, a piece of tape of integer length tt

placed at some position ss

will cover segments s,s+1,…,s+t−1s,s+1,…,s+t−1

.You are allowed to cover non-broken segments; it is also possible that some pieces of tape will overlap.Time is money, so you want to cut at most kk

continuous pieces of tape to cover all the broken segments. What is the minimum total length of these pieces?InputThe first line contains three integers nn

, mm

and kk

(1≤n≤1051≤n≤105

, n≤m≤109n≤m≤109

, 1≤k≤n1≤k≤n

) — the number of broken segments, the length of the stick and the maximum number of pieces you can use.The second line contains nn

integers b1,b2,…,bnb1,b2,…,bn

(1≤bi≤m1≤bi≤m

) — the positions of the broken segments. These integers are given in increasing order, that is, b1<b2<…<bnb1<b2<…<bn

.OutputPrint the minimum total length of the pieces.ExamplesInputCopy4 100 2
20 30 75 80
OutputCopy17
InputCopy5 100 3
1 2 4 60 87
OutputCopy6
NoteIn the first example, you can use a piece of length 1111

to cover the broken segments 2020

and 3030

, and another piece of length 66

to cover 7575

and 8080

, for a total length of 1717

.In the second example, you can use a piece of length 44

to cover broken segments 11

, 22

and 44

, and two pieces of length 11

to cover broken segments 6060

and 8787

.

#include <iostream>
#include <bits/stdc++.h>
using namespace std;
long long int n[100010];
long long int m[100010];
int main()
{
    long long int a,b,c,t,i;
    cin >> a >> b >> c;
    for(i=1;i<=a;i++)
    {
        cin >> n[i];
    }
    for(i=1;i<=a-1;i++)
    {
        m[i]=n[i+1]-n[i]+1;
    }
    sort(m+1,m+a);
    t=n[a]-n[1]+1;
    for(i=a-1;i>a-c;i--)
    {
        t=t-m[i];
    }
    cout << t+((c-1)*2);
    return 0;
}
内容概要:本文介绍了ENVI Deep Learning V1.0的操作教程,重点讲解了如何利用ENVI软件进行深度学习模型的训练与应用,以实现遥感图像中特定目标(如集装箱)的自动提取。教程涵盖了从数据准备、标签图像创建、模型初始化与训练,到执行分类及结果优化的完整流程,并介绍了精度评价与通过ENVI Modeler实现一键化建模的方法。系统基于TensorFlow框架,采用ENVINet5(U-Net变体)架构,支持通过点、线、面ROI或分类图生成标签数据,适用于多/高光谱影像的单一类别特征提取。; 适合人群:具备遥感图像处理基础,熟悉ENVI软件操作,从事地理信息、测绘、环境监测等相关领域的技术人员或研究人员,尤其是希望将深度学习技术应用于遥感目标识别的初学者与实践者。; 使用场景及目标:①在遥感影像中自动识别和提取特定地物目标(如车辆、建筑、道路、集装箱等);②掌握ENVI环境下深度学习模型的训练流程与关键参数设置(如Patch Size、Epochs、Class Weight等);③通过模型调优与结果反馈提升分类精度,实现高效自动化信息提取。; 阅读建议:建议结合实际遥感项目边学边练,重点关注标签数据制作、模型参数配置与结果后处理环节,充分利用ENVI Modeler进行自动化建模与参数优化,同时注意软硬件环境(特别是NVIDIA GPU)的配置要求以保障训练效率。
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